import torch
import torch.nn as nn
from torch.nn import functional as F

class GPTModel(nn.Module):
    def __init__(self, config, vocab_size):
        super().__init__()
        self.config = config
        
        self.token_embedding = nn.Embedding(vocab_size, config.n_embeds)
        self.position_embedding = nn.Embedding(config.block_size, config.n_embeds)
        self.blocks = nn.ModuleList([
            TransformerBlock(config) for _ in range(config.n_layers)
        ])
        self.ln_f = nn.LayerNorm(config.n_embeds)
        self.lm_head = nn.Linear(config.n_embeds, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape
        
        tok_emb = self.token_embedding(idx)
        pos_emb = self.position_embedding(torch.arange(T, device=idx.device))
        x = tok_emb + pos_emb
        
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)
        logits = self.lm_head(x)
        
        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)
            
        return logits, loss

class TransformerBlock(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.ln1 = nn.LayerNorm(config.n_embeds)
        self.ln2 = nn.LayerNorm(config.n_embeds)
        self.attn = MultiHeadAttention(config)
        self.mlp = FeedForward(config)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x):
        # Self-attention with residual connection
        x = x + self.dropout(self.attn(self.ln1(x)))
        # FFN with residual connection
        x = x + self.dropout(self.mlp(self.ln2(x)))
        return x

class MultiHeadAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.n_heads = config.n_heads
        self.head_size = config.n_embeds // config.n_heads
        self.n_embeds = config.n_embeds
        
        # Single linear layer for Q, K, V projections
        self.c_attn = nn.Linear(config.n_embeds, 3 * config.n_embeds)
        self.c_proj = nn.Linear(config.n_embeds, config.n_embeds)
        self.dropout = nn.Dropout(config.dropout)
        
        # Causal mask to prevent attending to future tokens
        self.register_buffer(
            "mask", 
            torch.tril(torch.ones(config.block_size, config.block_size))
            .view(1, 1, config.block_size, config.block_size)
        )

    def forward(self, x):
        B, T, C = x.shape
        
        # Calculate Q, K, V with a single linear projection
        q, k, v = self.c_attn(x).split(self.n_embeds, dim=2)
        
        # Reshape to (B, nh, T, hs)
        q = q.view(B, T, self.n_heads, self.head_size).transpose(1, 2)
        k = k.view(B, T, self.n_heads, self.head_size).transpose(1, 2)
        v = v.view(B, T, self.n_heads, self.head_size).transpose(1, 2)

        # Compute attention scores
        att = (q @ k.transpose(-2, -1)) * (1.0 / torch.sqrt(torch.tensor(self.head_size)))
        
        # Apply causal mask
        att = att.masked_fill(self.mask[:,:,:T,:T] == 0, float('-inf'))
        att = F.softmax(att, dim=-1)
        att = self.dropout(att)
        
        # Apply attention to values
        y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
        
        # Reshape and project back
        y = y.transpose(1, 2).contiguous().view(B, T, C)  # (B, T, C)
        y = self.c_proj(y)
        
        return y

class FeedForward(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.n_embeds, 4 * config.n_embeds),
            nn.GELU(),
            nn.Linear(4 * config.n_embeds, config.n_embeds),
            nn.Dropout(config.dropout),
        )

    def forward(self, x):
        return self.net(x)